5 Department of Epidemiology and Public Health and Medicine, Yong Loo Lin School of Medicine, National University of Singapore Block MD3 #03-17, 16, Medical Drive, Singapore. 117597 and Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts, 02115, USA

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Higher coffee consumption has been associated with a lower risk of type 2 diabetes
in cohort studies, but the physiological pathways through which coffee affects glucose
metabolism are not fully understood. The aim of this study was to evaluate the associations
between habitual coffee and tea consumption and glucose metabolism in a multi-ethnic
Asian population and possible mediation by inflammation.

Conclusions

These data provide additional evidence for a beneficial effect of habitual caffeinated
coffee consumption on insulin sensitivity, and suggest that this effect is unlikely
to be mediated by anti-inflammatory mechanisms.

Background

The prevalence of Type-2 diabetes mellitus (Type-2 DM) is rising rapidly in Asian
countries [1,2], and identifying lifestyle measures to improve insulin sensitivity and prevent Type-2
DM in these populations is particularly important. Data from multiple prospective
cohort studies [3], including results from Asian populations [4,5], indicate that coffee consumption is associated with a lower risk of Type-2 DM. Results
from several cross-sectional studies suggest that an association between coffee consumption
and insulin sensitivity may contribute to this association [6,7]. Because coffee is a rich source of anti-oxidants [8], and sub-clinical inflammation has been implicated in the development of insulin
resistance (IR) [9] coffee may improve insulin sensitivity by decreasing inflammation. Inflammatory markers
including higher C-reactive protein (CRP) and lower adiponectin concentrations have
been associated with insulin resistance [10,11].

However, studies on coffee consumption and inflammatory outcomes have yielded inconsistent
results. Coffee consumption was associated with lower plasma CRP concentrations in
four cross-sectional studies [12-15], with higher CRP concentrations in a Greek population [16], and with no change in CRP concentrations in a small clinical study [17]. Fewer studies have examined coffee consumption in relation to adiponectin concentrations.
In cross-sectional studies in the U.S. [18] and Japan [19] coffee consumption was associated with higher adiponectin concentrations, whereas
another study in Japan [20] observed no such association. In an intervention study, serum adiponectin concentration
increased when participants consumed eight cups of coffee per day as compared to when
they did not consume coffee [17]. These studies did not distinguish between high-molecular weight and lower molecular
weight adiponectin that may have different metabolic effects [21].

There is less consistent evidence on the association between tea consumption and risk
of Type-2 DM [3]. Data on the effects of tea in relation to glycemic parameters and inflammatory markers
are inconclusive [22] and are mostly limited to green tea [23-26]. Since the putative bioactive compounds in tea vary by type, some of the discrepancies
may be related to the type of tea consumed.

The main aim of this study was to evaluate consumption of coffee and three type of
tea (black, green and Oolong) in relation to markers of inflammation, insulin sensitivity,
and glycemia in a multi-ethnic population consisting of Chinese, Malays and Asian-Indians.
We hypothesized that coffee and tea consumption are associated with lower IR and that
this association can be partly explained by plasma adiponectin and CRP concentrations.

Methods

Study Population

In this study we used cross-sectional data from the Singapore Prospective study-2
(SP2) cohort. This study conducted between 2003-2007, was a follow-up of persons participating
in four previous Singaporean population-based studies: the Thyroid and Heart Study
[27], the National Health Survey-1992 [28], National University of Singapore Heart Study [29] and the National Health Survey-1998 [30] which were conducted between 1982 and 1998. These studies used a stratified random
sampling method, disproportionate for ethnicity to increase representation of the
minority Malay and Asian-Indian ethnic groups. The total participant pool derived
from these studies consisted of 10,747 persons, of which 667 were not available (deceased,
emigrated, errors in identity number recording) [31]. For the SP2 study, an interviewer-administered questionnaire was completed during
a home visit by 7744 participants, and 5163 of these participants attended a clinic
visit. Data on demographic and lifestyle characteristics and medical history were
collected during the interview and fasting blood samples were obtained during the
clinic visit for measuring biochemical markers [32]. For the present analyses, we only included people who attended the clinic visit
(n = 5163). We excluded data from participants who reported pre-existing diabetes
(n = 494), cardiovascular disease (n = 213) and current cancer (n = 56) or those for
whom these data were missing (n = 76). We also excluded people who reported their
race as 'other' (n = 3) and those who were pregnant (n = 2). Of the remaining 4417
people, 220 had an implausible energy intake (> 7000 or < 400 kcal/day, or who had
an energy intake to energy expenditure ratio that fell in extreme 2.5 percentile range)
and 35 people had reported changing their beverage intake in the month preceding the
questionnaire. After excluding these people, 22 had missing covariate data. After
these exclusions our final dataset comprised of 4139 participants. The study was approved
by the Singapore General Hospital and the National University Hospital Institutional
Review Boards.

Dietary data

Dietary intake data during the month preceding the interview was assessed using a
semi-quantitative 169-item validated food frequency questionnaire that is used in
the National Nutrition Surveys [33]. Participants were asked to estimate their frequency of consuming each food group,
based on a standard portion size specific for that food group. Participants could
report consumption per day, per week or per month, or could report to never or rarely
consume a food. Nutrient intakes were computed by the Health Promotion Board of Singapore,
using their in-house database.

Data on coffee intake were obtained by asking participants about the habitual amount
of coffee consumed. Participants could select one of seven responses ranging from
'never/rarely' to '10 or more cups per day', with 1 cup being defined as a "standard
coffee-shop cup" of 215 ml. Similar questions were asked about green, Oolong and black
tea. Coffee intake was grouped into four categories (never/rarely, < 1 cup/day, 1-2
cups/day and ≥ 3 cups per day) and tea intake into 3 categories (< 1 cup/week, 2-6
cups/wk, ≥ 1 cup per day) for data analyses to avoid categories with small numbers.
The questionnaire also elicited information on the amount of sugar and the type and
amount of milk/cream added to coffee and tea. Although data on type of coffee were
not collected, decaffeinated coffee intake is very low in the Singapore population
[4].

Assessment of covariates

Height was measured using a wall mounted measuring tape and weight was measured using
a digital scale. BMI was computed as weight (kg) divided by height (m2). Physical activity level and smoking status was assessed from the questionnaire
and alcohol intake was obtained from the FFQ. Total physical activity was assessed
using a validated questionnaire on household, occupational, leisure-time and transport
activity [34]. Participants who used medications to lower cholesterol or triglycerides or increase
HDL-C concentrations, or who reported having been diagnosed with hypercholesterolemia
were considered to have a history of dyslipidemia. Participants who used anti-hypertensive
medication or reported having been diagnosed by a physician with hypertension were
considered to have a history of hypertension.

Statistical Analyses

All response variables were transformed using natural logarithms to approximate normality.
The means and 95% confidence intervals (CIs) were then exponentiated to obtain geometric
means and 95% CIs. Values for response variables that were more than four standard
deviations from the mean were considered outliers and were excluded from the analyses.
The HOMA-IR index was computed as fasting plasma glucose mmol/L × fasting serum insulin
(mU/L)/22.5 and the HOMA-beta index was computed as 20 × fasting serum insulin (mU/L)/fasting
plasma glucose (mmol/L -3.5) [35]. Participant characteristics were compared across categories of coffee and tea using
Kruskal-Wallis (for non-normally distributed continuous variables), analysis of variance
(for normally distributed continuous variables) or the chi-squared test (for categorical
variables). Multiple linear regression analyses were performed using HOMA-IR, HOMA-beta,
plasma glucose, HbA1c, plasma insulin, CRP, total adiponectin and HMW adiponectin
as dependant variables and coffee or tea as independent variables. Linear estimators
were fitted using two multivariate models: Model 1:adjusted for age, sex and ethnicity
Model 2: further adjusted for BMI (kg/m2), physical activity (kcal/week), education level, alcohol consumption, cigarette
smoking, history of dyslipidemia, history of hypertension and dietary factors i.e.
energy intake (kcal), fiber (per 1000 kcal), cholesterol (per 1000 kcal), PUFA (%
energy), MUFA (% energy), SFA (% energy), coffee (for tea analyses) and tea (for coffee
analyses). HOMA-beta, as a marker for beta cell function must be considered within
the context of IR, because insulin sensitivity regulates insulin secretion [36]. Models for HOMA-beta were therefore further adjusted for HOMA-IR. Residual plots
and Cooks distance test (Cooks D < 1.0) was used to assess if there were influential
points in the multivariate models [37]. Influential points were not observed with a maximum Cooks Di of 0.03. For trend
tests, the median number of cups of coffee or tea consumed in each coffee category
or each tea category was treated as a continuous variable. We tested for interactions
with overweight status by including multiplicative interaction terms in the model
after adjusting for all potential confounders. To evaluate whether additions to coffee
modified observed associations, stratified analyses were performed for groups of participants
that reportedly added or did not add milk to coffee, and for those that reportedly
added or did not add sugar to beverages excluding individuals who used the other type
of coffee. Because strong correlations were noted between HOMA-IR and fasting insulin
(rspearman = 0.98) and between total and HMW adiponectin (rspearman = 0.94), results are not presented for fasting insulin and total adiponectin. All
data were analyzed using SPSS v11 (SPSS Inc. Chicago, IL). P-values < 0.05 were considered
statistically significant

Results

Participant Characteristics

Coffee drinkers were more likely to be older, male, non-tea drinkers, alcohol consumers,
cigarette smokers with higher physical activity, a lower education level, and less
healthy dietary choices (Table 1). Most coffee drinkers consumed their coffee with milk/cream (71.4%) and sugar (63.0%)
Characteristics of tea drinkers varied by type of tea consumed (Additional file 1Table S1). Black tea and Oolong tea drinkers were more likely to be older, male and have higher
BMI. Green tea consumers were more likely to be younger. Chinese were more likely
to consume Oolong or green tea, and less likely to consume black tea than Indians
or Malay. Black tea consumption was associated with less health conscious dietary
intakes, and more physical activity. Oolong and green tea drinkers were more likely
to consume alcohol. Regardless of tea type, tea drinkers were more likely to be highly
educated and to have known hypertension.

Associations between tea and glycemic and inflammatory markers

Black, Oolong, and green tea consumption were not significantly associated with glycemic
markers in fully adjusted models (Tables 3, 4 and 5). Tea consumption was not significantly associated with inflammatory markers in age,
sex and ethnicity adjusted models. However after adjustment for other potential confounders,
green tea consumption was significantly associated with lower CRP concentrations (percent
difference: - 12.2% for ≥ 1 cup/day versus < 1 cup/week; Ptrend = 0.042) (Table 5). This association was not modified by BMI status (Pinteraction = 0.1032) or ethnicity (Pinteraction = 0.939). Few people consumed three or more cups of a given tea-type (N = 32, 71 and
98 for green, Oolong and black tea respectively), making it less meaningful to look
at higher categories of tea intake. When we summed tea intake across the different
types of tea, we found no significant associations between tea consumption and metabolic
markers (Additional file 1Table S2).

Discussion

In this study, in a multi-ethnic Singaporean population, coffee consumption was inversely
associated with IR independent of plasma CRP or adiponectin concentrations. This association
appeared to be consistent for overweight and non-overweight participants and for Chinese,
Malay, and Asian Indian participants. We also noted an inverse relationship between
green tea and plasma CRP concentrations, but found no evidence for an association
between tea consumption and basal glucose metabolism.

To the best of our knowledge this is the first study to examine the extent to which
inflammatory markers such as CRP and adiponectin explain the association between coffee
consumption and IR. Data from other clinical and cross-sectional studies suggest a
beneficial effect of coffee consumption on insulin sensitivity. However few studies
have examined coffee in relation to insulin sensitivity [6] or inflammation [13,15,19,20] in Asian populations and these studies were all conducted in Japan. These cross-sectional
studies found inverse associations between coffee and serum CRP concentrations [13,15], and either no association [20] or a direct association [19] with serum adiponectin concentrations. Both adiponectin and CRP have been associated
with Type-2 DM risk [38,39]. Adiponectin can also decrease IR, independent of its anti-inflammatory properties,
via the AMP-activated protein kinase pathway [40].

Although we observed an inverse association between coffee consumption and IR, we
found no significant associations between coffee intake and inflammatory markers and
these markers did not explain the association between coffee and IR. These results
suggest that the putative protective effect of coffee consumption against the development
of Type-2 DM, which has been previously reported in Singapore Chinese [4], is at least partly mediated by its effects on IR. However, this inverse association
between coffee consumption and IR is probably not mediated by anti-inflammatory effects.
Nevertheless, there are some caveats that are worth considering. First, although CRP
is a well-established marker of systemic inflammation [41], it is possible that coffee alters other measures of inflammation that were not examined
in this study. Second, the relatively low amounts of coffee consumed in this population,
may have been insufficient to exert a biological effect on CRP and adiponectin. In
a cross-sectional study of U.S. women, only consuming four or more cups of coffee
per day, but not lower consumption, was associated with higher adiponectin concentrations
[18]. However, the literature on this topic is not consistent with a few other studies
suggesting associations with concentrations of CRP and adiponectin at intakes as low
as one cup per day [13,19].

Apart from its anti-inflammatory effects, phenolic compounds in coffee have been postulated
to affect glucose metabolism through various mechanisms including intestinal glucose
absorption and incretin secretion [8], and reduction of hepatic triglyceride accumulation [8]. Consistent with several intervention studies, no associations were noted between
black or green tea, and fasting glycemic parameters in this investigation [23,24,26]. In contrast, a Japanese intervention study, noted reduced fasting plasma glucose
and fructosamine concentrations in diabetic participants who consumed 1 L per day
of Oolong tea for four weeks [42]. The difference in diabetes status or amount of tea consumed may have contributed
to the difference in results as compared with our study.

Tea consumption was not associated with adiponectin concentrations in our study. This
is consistent with studies that showed no associations between green tea and plasma
adiponectin concentration even at relatively high intake levels of four cups/day [19,43], but contrasts with one randomized trial in which consumption of one L of Oolong
tea per day increased adiponectin concentrations in 22 persons with coronary artery
disease [44].

The inverse association between green tea and CRP is a finding that stands in contrast
to several other studies which observed no such association [25,43,45,46]. However, these studies were all clinical trials that examined either the acute [46] or short-term (4-8 weeks) [25,43,45] effects of green tea intake. A longer duration may be needed for the beneficial effects
of green tea on CRP to be manifested. Interestingly, we found no associations between
black tea and glycemic parameters or inflammatory markers. These findings were inconsistent
with those of a prospective cohort study in Singaporean Chinese that found an inverse
association between black tea consumption and Type-2 DM risk [4]. It is possible that black tea alters post-prandial aspects of glucose metabolism,
which were not examined in this study.

The large multi-ethnic study population and detailed assessments of potential confounders
are strengths of this study. Because of the cross-sectional design, the sequence of
events cannot be inferred from this study. However, we excluded people with known
diseases who may have exhibited differential recall of lifestyle exposures or were
on medications that may have obscured the effects of coffee or tea on the markers
of interest. Also, measurement error in the assessment of lifestyle exposures is unavoidable
and makes the possibility of residual confounding a concern. However, as coffee consumption
was associated with less health-conscious lifestyle behaviors, it is less likely that
un-measured confounders would weaken the inverse association between coffee and IR.
Green tea intake was associated with some favorable lifestyle behaviors and higher
education, and it is thus possible that green tea may have served as a proxy for an
unmeasured or imperfectly measured beneficial exposure in this study.

Conclusions

These data suggest a beneficial effect of coffee consumption on insulin sensitivity
in Asians at modest levels of consumption. This association did not appear to be mediated
by anti-inflammatory mechanisms, and other pathways should be considered for mechanistic
studies.

Authors' contributions

EST and JL designed the cohort study and directed its implementation. CHC and WX conducted
the data analysis. SAR conducted the literature review, helped in data analysis and
drafted the manuscript. NN helped in data cleaning and conducted technical review.
KSC conceived of the research question and guided data analyses. RMVD guided data
analyses and interpretation and led manuscript writing. All authors read and approved
the final manuscript.

Acknowledgements

The study was supported by the following grants: Biomedical Research Council (03/1/27/18/216),
National Medical Research Council (0838/2004) and (NMRC/CSI/0002/2005). Study sponsors
were not involved in data collection or manuscript preparation. The authors also gratefully
acknowledge the Health Promotion Board, Singapore for use of their food frequency
questionnaire and database for dietary analyses.